(137f) Reduced Order-Discrete Element Method Modeling of Comilling for Efficient Integration into Continuous Process
Comilling unit in the dry and wet granulation routes in pharmaceutical manufacturing is used to break granules into a required size distribution.Â Particle size reduction in comill through the impact of a rotating impeller on granules and their eventual exit through a screen is a complex phenomenon. Past work on predicting the breakage has used Population balance models (PBM) where the kernel that represents the probability of granule breakage is represented semi-empirically through their dependence on impeller speed, granule size etc. A mechanistic understanding of the breakage phenomenon can be achieved through discrete element method (DEM) modeling of breakage in comill where particle-particle, particle-wall interactions are captured through underlying system of equations based on Newtonâs laws of motion. The DEM model proposed in  takes into account a threshold impact energy above which the granules break. A multi scale framework is established where cumulative energy of granules from DEM simulations are utilized in an energy based PBM kernel and an iterative algorithm is used for estimating material specific parameters in the kernel. However, the high computational expense of DEM simulations and the required cumbersome post processing limits its use in any predictive capacity for continuous manufacturing such as integration into flowsheet model. Reduced order modeling can bridge this gap .
Current work proposes a reduced order-discrete element method modeling to represent the multi- dimensional and time dependent energy data from DEM simulations. The methodology reduces the computational expense associated with the high fidelity DEM simulations while retaining accuracy of the mechanistic data . DEM simulations of comilling are run at various flow rates and impeller speeds based on an experimental design and the energy distribution data from these simulations are used to build a reduced order model. An analytical relationship that captures dependence of energy of granules on size, time, flow rate and impeller speed is developed. This enables accurate representation of energy based kernel in PBM model. A comparison of various surrogate modeling techniques such as kriging, radial basis functions is also presented. The improvement achieved through this framework is drastic as the DEM simulations take approximately five days where as the reduced order DEM model incorporated into PBM takes few seconds. Through this work, a framework to represent the mechanistic information from DEM efficiently into PBM of a comill operation is established, thus enabling its incorporation into flowsheet modeling of DG and WG routes of continuous manufacturing. The incorporation of reduced order model into a continuous WG route flowsheet model is also demonstrated.
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